Statistics in medicine
-
Statistics in medicine · Apr 2003
Causal logistic models for non-compliance under randomized treatment with univariate binary response.
We propose a method for estimating the marginal causal log-odds ratio for binary outcomes under treatment non-compliance in placebo-randomized trials. This estimation method is a marginal alternative to the causal logistic approach by Nagelkerke et al. (2000) that conditions on partially unknown compliance (that is, adherence to treatment) status, and also differs from previous approaches that estimate risk differences or ratios in subgroups defined by compliance status. The marginal causal method proposed in this paper is based on an extension of Robins' G-estimation approach for fitting linear or log-linear structural nested models to a logistic model. ⋯ These models differ in the way that compliance is related to potential outcomes, and thus differ in the way that the causal effect is identified. The simulations also show that the proposed marginal causal estimation approach performs well in terms of bias under the different levels of confounding due to non-adherence and under different causal logistic models. We also provide results from the analyses of two data sets further showing how a comparison of the marginal and conditional estimators can help evaluate the magnitude of confounding due to non-adherence.
-
Statistics in medicine · Apr 2003
Robustness and power of analysis of covariance applied to ordinal scaled data as arising in randomized controlled trials.
In clinical trials comparing two treatments, ordinal scales of three, four or five points are often used to assess severity, both prior to and after treatment. Analysis of covariance is an attractive technique, however, the data clearly violate the normality assumption and in the presence of small samples, and large sample theory may not apply. The robustness and power of various versions of parametric analysis of covariance applied to small samples of ordinal scaled data are investigated through computer simulation. ⋯ The hierarchical approach which first tests for homogeneity of regression slopes and then fits separate slopes if there is significant non-homogeneity produced significance levels that exceeded the nominal levels especially when the sample sizes were small. The model which assumes homogeneous regression slopes produced the highest power among competing tests for all of the configurations investigated. The t-test on difference scores also produced good power in the presence of small samples.
-
Statistics in medicine · Mar 2003
Controlling type I error rate for fast track drug development programmes.
The U. S. Food and Drug Administration (FDA) Modernization Act of 1997 has a Section (No. 112) entitled 'Expediting Study and Approval of Fast Track Drugs' (the Act). ⋯ Since then many health products have reached patients who suffered from AIDS, cancer, osteoporosis, and many other diseases, sooner by utilizing the Fast Track Act and the FTDD programmes. In the meantime several scientific issues have also surfaced when following the FTDD programmes. In this paper we will discuss the concept of two kinds of type I errors, namely, the 'conditional approval' and the 'final approval' type I errors, and propose statistical methods for controlling them in a new drug submission process.
-
Statistics in medicine · Feb 2003
Applications of continuous time hidden Markov models to the study of misclassified disease outcomes.
Disease progression in prospective clinical and epidemiological studies is often conceptualized in terms of transitions between disease states. Analysis of data from such studies can be complicated by a number of factors, including the presence of individuals in various prevalent disease states and with unknown prior disease history, interval censored observations of state transitions and misclassified measurements of disease states. We present an approach where the disease states are modelled as the hidden states of a continuous time hidden Markov model using the imperfect measurements of the disease state as observations. ⋯ Applications to two binary disease outcomes are presented: the oral lesion hairy leukoplakia in a cohort of HIV infected men and cervical human papillomavirus (HPV) infection in a cohort of young women. Estimated transition rates and misclassification probabilities for the hairy leukoplakia data agree well with clinical observations on the persistence and diagnosis of this lesion, lending credibility to the interpretation of hidden states as representing the actual disease states. By contrast, interpretation of the results for the HPV data are more problematic, illustrating that successful application of the hidden Markov model may be highly dependent on the degree to which the assumptions of the model are satisfied.
-
Statistics in medicine · Dec 2002
Comparative StudyUse of the mean, hot deck and multiple imputation techniques to predict outcome in intensive care unit patients in Colombia.
A cohort of intensive care unit (ICU) patients in 20 Colombian ICUs is used to describe the application of three imputation techniques: single, hot deck and multiple imputation. These strategies were used to impute the missing data in the variables used to construct APACHE II scores, a scoring system for the ICU patients that provides an unbiased standardized estimate of the probability of hospital death. ⋯ The area under the receiver operating characteristic (ROC) curve was used to compare imputation strategies with respect to predictive power. While statistically significant differences were found for the area under the ROC curve, these differences were not clinically significant.